Relevant Coursework: Data Structures & Algorithms, Analysis of Algorithms, Approximation Algorithms, Computer Architecture, Machine Learning & Data Mining, Distributed Systems, Database Systems

Languages: Python, C++, JavaScript/TypeScript, SQL (PostgreSQL)

Frameworks/Tools: PyTorch, React, Node.js, Express, Distributed Compute (Hive), Git, Docker, Linux, AI Agents (Antigravity, Gemini 3.5 Pro)

• Developing a deep learning data pipeline using PyTorch to isolate and extract neural EEG signals from noisy cochlear implant electrical artifacts.
• Orchestrating a hybrid compute workflow for large-scale data processing, prototyping models locally before scaling custom dataset training across a distributed Hive cluster.
• Designing 1D U-Net architectures and employing blind source separation methodologies to analyze high-volume frequency-domain data.

• Evaluated assignments and provided structured feedback on algorithmic correctness and complexity analysis for over 100 students in Analysis of Algorithms.
• Led small-group sessions breaking down complex discrete mathematics and logical reasoning concepts.

• Engineered a highly specialized data pipeline to programmatically parse, clean, and normalize execution telemetry from high-volume, unstructured software logs.
• Built an Abstract Syntax Tree (AST) parser to transform messy, real-world software artifacts into structured, queryable formats for machine learning research.
• Directed Antigravity agents powered by Gemini 3.5 Pro to analyze artifact structures, determining the optimal heuristic strategy for processing complex logs.
• Integrated Large Language Models to automate the classification of complex software vulnerabilities with high precision.

• Deployed formal verification tools including Dafny and Z3 to provide rigorous mathematical proofs of Mahjong winning hand verification.
• Authored a comprehensive verification model for complex scoring logic to validate system reliability and fault tolerance through formal methods.

• Designed and deployed a live scoring infrastructure for athletic tournaments to streamline real-time data ingestion with Node.js.
• Engineered a synchronization engine linking Google Sheets with a live server via webhooks to ensure immediate frontend state updates.
• Accelerated frontend UI development by leveraging Antigravity and Gemini 3.5 Pro to rapidly prototype and integrate user-facing scoring components.

• Architected a backend-driven experimental simulation platform for economics research utilizing Python and PostgreSQL.
• Designed and deployed automated trading agents to model diverse market strategies within high-frequency asset exchange scenarios.
• Engineered robust data ingestion and real-time visualization components to track market fluctuations and agent behavior continuously.

• Engineered machine learning architectures for image synthesis, applying manifold learning and dimensionality reduction to evaluate reconstruction fidelity.
• Utilized Vision Transformers and SimCLR self-supervised frameworks to optimize representation learning on complex visual structures.

• Direct weekly training sessions, mentoring beginner athletes in foundational techniques and competitive forms.
• Engineered a real-time scheduling and live scoring platform using Node.js and webhooks to synchronize tournament logistics via Google Sheets.